Functional connectivity and causal connections across different neural units are two main categories for how fMRI data on brain connectivity patterns are categorized. Recently, computational techniques—especially those based on graph theory—have been crucial in helping us comprehend the structure of brain connections.
In an effort to understand the neural bases of human cognition and neurological illnesses, a team at the University of Florida conducted a systematic review of how brain features might arise through the interactions of different neural units in various cognitive and neurological applications utilizing fMRI. This was made possible by the development of graph theoretical analysis.
A central and enduring aim of research in the psychological and brain sciences is to elucidate the information-processing architecture of human intelligence. Does intelligence originate from a specific brain structure or instead reflect system-wide network mechanisms for flexible and efficient information processing?
As neuroscientists work to comprehend the entire information behind cognition, behavior, and perception, studies on modeling the human brain as a complex system have significantly increased. The anatomical, functional, and causal structure of the human brain can be better understood by looking at connection patterns in the brain. Functional and effective connection among the connectivity methods have recently become the subject of computational investigations.
The topological design of human brain networks, including small-worldness, modularity, and highly linked or centralized hubs, may be understood by graph-based network analysis. Some networks have the trait of small-worldness, where most nodes can be reached from every other node with a minimal number of steps even when most of them are not neighbors. This property indicates effective information segregation and integration in the human brain networks with minimal energy and wiring costs, making it ideally suited for the study of complex brain dynamics.
The researchers provided in-depth information on graph theoretical applications in neuroscience as well as the study of connection patterns in the complex brain network. The study’s findings indicate that graph theory and its applications to cognitive neuroscience are highly effective at describing the behavior of complex brain systems.
Cognitive function, behavioral variety, experimental task, and neurological conditions including epilepsy, Alzheimer’s disease, multiple sclerosis, autism, and attention-deficit/hyperactivity disorder are all likely to have an impact on the brain network architecture. The topological patterns of brain networks may be identified using graph theory metrics such as node degree, clustering coefficient, average path length, hubs, centrality, modularity, robustness, and assortativity, which can serve as indicators of cognitive and behavioral performance.
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review, Farzad V. Farahani, Waldemar Karwowski, and Nichole R. Lighthall
Published: June 2019